OpenRaiser/Intern-Atlas huggingface dataset:
The Defense of Networked Targets in General Lotto games
Venue: arXiv cs.GT 2023
Year: 2023

Ensuring the security of networked systems is a significant problem, considering the susceptibility of modern infrastructures and technologies to adversarial interference. A central component of this problem is how defensive resources should be allocated to mitigate the severity of potential attacks on the system. In this paper, we consider this in the context of a General Lotto game, where a defender and attacker deploys resources on the nodes of a network, and the objective is to secure as many links as possible. The defender secures a link only if it out-competes the attacker on both of its associated nodes. For bipartite networks, we completely characterize equilibrium payoffs and strategies for both the defender and attacker. Surprisingly, the resulting payoffs are the same for any bipartite graph. On arbitrary network structures, we provide lower and upper bounds on the defender's max-min value. Notably, the equilibrium payoff from bipartite networks serves as the lower bound. These results suggest that more connected networks are easier to defend against attacks. We confirm these findings with simulations that compute deterministic allocation strategies on large random networks. This also highlights the importance of randomization in the equilibrium strategies.
RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge
Venue: arXiv cs.NE 2023
Year: 2023

Deep Neural Network (DNN) based inference at the edge is challenging as these compute and data-intensive algorithms need to be implemented at low cost and low power while meeting the latency constraints of the target applications. Sparsity, in both activations and weights inherent to DNNs, is a key knob to leverage. In this paper, we present RAMAN, a Re-configurable and spArse tinyML Accelerator for infereNce on edge, architected to exploit the sparsity to reduce area (storage), power as well as latency. RAMAN can be configured to support a wide range of DNN topologies - consisting of different convolution layer types and a range of layer parameters (feature-map size and the number of channels). RAMAN can also be configured to support accuracy vs power/latency tradeoffs using techniques deployed at compile-time and run-time. We present the salient features of the architecture, provide implementation results and compare the same with the state-of-the-art. RAMAN employs novel dataflow inspired by Gustavson's algorithm that has optimal input activation (IA) and output activation (OA) reuse to minimize memory access and the overall data movement cost. The dataflow allows RAMAN to locally reduce the partial sum (Psum) within a processing element array to eliminate the Psum writeback traffic. Additionally, we suggest a method to reduce peak activation memory by overlapping IA and OA on the same memory space, which can reduce storage requirements by up to 50%. RAMAN was implemented on a low-power and resource-constrained Efinix Ti60 FPGA with 37.2K LUTs and 8.6K register utilization. RAMAN processes all layers of the MobileNetV1 model at 98.47 GOp/s/W and the DS-CNN model at 79.68 GOp/s/W by leveraging both weight and activation sparsity.
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
Venue: arXiv cs.LG 2023
Year: 2023

Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain. We approach this problem and propose a novel encoding framework that relies on self-supervised learning with momentum contrast to learn representations from multivariate time-series of various physiological domains without needing labels. Our model uses a transformer architecture that can be easily adapted to classification problems by optimizing a linear output classification layer. We experimentally evaluate our framework using two publicly available physiological datasets from different domains, i.e., human activity recognition from embedded inertial sensory and emotion recognition from electroencephalography. We show that our self-supervised learning approach can indeed learn discriminative features which can be exploited in downstream classification tasks. Our work enables the development of domain-agnostic intelligent systems that can effectively analyze multivariate time-series data from physiological domains.
Herd's Eye View: Improving Game AI Agent Learning with Collaborative Perception
Venue: arXiv cs.MA 2023
Year: 2023

We present a novel perception model named Herd's Eye View (HEV) that adopts a global perspective derived from multiple agents to boost the decision-making capabilities of reinforcement learning (RL) agents in multi-agent environments, specifically in the context of game AI. The HEV approach utilizes cooperative perception to empower RL agents with a global reasoning ability, enhancing their decision-making. We demonstrate the effectiveness of the HEV within simulated game environments and highlight its superior performance compared to traditional ego-centric perception models. This work contributes to cooperative perception and multi-agent reinforcement learning by offering a more realistic and efficient perspective for global coordination and decision-making within game environments. Moreover, our approach promotes broader AI applications beyond gaming by addressing constraints faced by AI in other fields such as robotics. The code is available at https://github.com/andrewnash/Herds-Eye-View
Learnable Digital Twin for Efficient Wireless Network Evaluation
Venue: arXiv cs.NI 2023
Year: 2023

Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration. In this paper, we propose a learning-based NDT for network simulators. The proposed method offers a holistic representation of information flow in a wireless network by integrating node, edge, and path embeddings. Through this approach, the model is trained to map the network configuration to KPIs in a single forward pass. Hence, it offers a more efficient alternative to traditional simulation-based methods, thus allowing for rapid experimentation and optimization. Our proposed method has been extensively tested through comprehensive experimentation in various scenarios, including wired and wireless networks. Results show that it outperforms baseline learning models in terms of accuracy and robustness. Moreover, our approach achieves comparable performance to simulators but with significantly higher computational efficiency.
Semantically-aware Mask CycleGAN for Translating Artistic Portraits to Photo-realistic Visualizations
Venue: arXiv cs.CV 2023
Year: 2023

Image-to-image translation (I2I) is defined as a computer vision task where the aim is to transfer images in a source domain to a target domain with minimal loss or alteration of the content representations. Major progress has been made since I2I was proposed with the invention of a variety of revolutionary generative models. Among them, GAN-based models perform exceptionally well as they are mostly tailor-made for specific domains or tasks. However, few works proposed a tailor-made method for the artistic domain. In this project, I propose the Semantic-aware Mask CycleGAN (SMCycleGAN) architecture which can translate artistic portraits to photo-realistic visualizations. This model can generate realistic human portraits by feeding the discriminators semantically masked fake samples, thus enforcing them to make discriminative decisions with partial information so that the generators can be optimized to synthesize more realistic human portraits instead of increasing the similarity of other irrelevant components, such as the background. Experiments have shown that the SMCycleGAN generate images with significantly increased realism and minimal loss of content representations.
Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems
Venue: arXiv cs.IR 2023
Year: 2023

Recommender system is a widely adopted technology in a diversified class of product lines. Modern day recommender system approaches include matrix factorization, learning to rank and deep learning paradigms, etc. Unlike many other approaches, learning to rank builds recommendation results based on maximization of the probability of ranking orders. There are intrinsic issues related to recommender systems such as selection bias, exposure bias and popularity bias. In this paper, we propose a fair recommender system algorithm that uses Poisson process and Skellam distribution. We demonstrate in our experiments that our algorithm is competitive in accuracy metrics and far more superior than other modern algorithms in fairness metrics.
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method
Venue: arXiv cs.CL 2023
Year: 2023

The large scale of pre-trained language models poses a challenge for their deployment on various devices, with a growing emphasis on methods to compress these models, particularly knowledge distillation. However, current knowledge distillation methods rely on the model's intermediate layer features and the golden labels (also called hard labels), which usually require aligned model architecture and enough labeled data respectively. Moreover, the parameters of vocabulary are usually neglected in existing methods. To address these problems, we propose a general language model distillation (GLMD) method that performs two-stage word prediction distillation and vocabulary compression, which is simple and surprisingly shows extremely strong performance. Specifically, GLMD supports more general application scenarios by eliminating the constraints of dimension and structure between models and the need for labeled datasets through the absence of intermediate layers and golden labels. Meanwhile, based on the long-tailed distribution of word frequencies in the data, GLMD designs a strategy of vocabulary compression through decreasing vocabulary size instead of dimensionality. Experimental results show that our method outperforms 25 state-of-the-art methods on the SuperGLUE benchmark, achieving an average score that surpasses the best method by 3%.
2-D SSM: A General Spatial Layer for Visual Transformers
Venue: arXiv cs.CV 2023
Year: 2023

A central objective in computer vision is to design models with appropriate 2-D inductive bias. Desiderata for 2D inductive bias include two-dimensional position awareness, dynamic spatial locality, and translation and permutation invariance. To address these goals, we leverage an expressive variation of the multidimensional State Space Model (SSM). Our approach introduces efficient parameterization, accelerated computation, and a suitable normalization scheme. Empirically, we observe that incorporating our layer at the beginning of each transformer block of Vision Transformers (ViT) significantly enhances performance for multiple ViT backbones and across datasets. The new layer is effective even with a negligible amount of additional parameters and inference time. Ablation studies and visualizations demonstrate that the layer has a strong 2-D inductive bias. For example, vision transformers equipped with our layer exhibit effective performance even without positional encoding
EaSyGuide : ESG Issue Identification Framework leveraging Abilities of Generative Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

This paper presents our participation in the FinNLP-2023 shared task on multi-lingual environmental, social, and corporate governance issue identification (ML-ESG). The task's objective is to classify news articles based on the 35 ESG key issues defined by the MSCI ESG rating guidelines. Our approach focuses on the English and French subtasks, employing the CerebrasGPT, OPT, and Pythia models, along with the zero-shot and GPT3Mix Augmentation techniques. We utilize various encoder models, such as RoBERTa, DeBERTa, and FinBERT, subjecting them to knowledge distillation and additional training. Our approach yielded exceptional results, securing the first position in the English text subtask with F1-score 0.69 and the second position in the French text subtask with F1-score 0.78. These outcomes underscore the effectiveness of our methodology in identifying ESG issues in news articles across different languages. Our findings contribute to the exploration of ESG topics and highlight the potential of leveraging advanced language models for ESG issue identification.
Reinforcement Learning with Parameterized Manipulation Primitives for Robotic Assembly
Venue: arXiv cs.RO 2023
Year: 2023

A common theme in robot assembly is the adoption of Manipulation Primitives as the atomic motion to compose assembly strategy, typically in the form of a state machine or a graph. While this approach has shown great performance and robustness in increasingly complex assembly tasks, the state machine has to be engineered manually in most cases. Such hard-coded strategies will fail to handle unexpected situations that are not considered in the design. To address this issue, we propose to find dynamics sequence of manipulation primitives through Reinforcement Learning. Leveraging parameterized manipulation primitives, the proposed method greatly improves both assembly performance and sample efficiency of Reinforcement Learning compared to a previous work using non-parameterized manipulation primitives. In practice, our method achieves good zero-shot sim-to-real performance on high-precision peg insertion tasks with different geometry, clearance, and material.
UAV Trajectory and Multi-User Beamforming Optimization for Clustered Users Against Passive Eavesdropping Attacks With Unknown CSI
Venue: arXiv cs.IT 2023
Year: 2023

This paper tackles the fundamental passive eavesdropping problem in modern wireless communications in which the location and the channel state information (CSI) of the attackers are unknown. In this regard, we propose deploying an unmanned aerial vehicle (UAV) that serves as a mobile aerial relay (AR) to help ground base station (GBS) support a subset of vulnerable users. More precisely, our solution (1) clusters the single-antenna users in two groups to be either served by the GBS directly or via the AR, (2) employs optimal multi-user beamforming to the directly served users, and (3) optimizes the AR's 3D position, its multi-user beamforming matrix and transmit powers by combining closed-form solutions with machine learning techniques. Specifically, we design a plain beamforming and power optimization combined with a deep reinforcement learning (DRL) algorithm for an AR to optimize its trajectory for the security maximization of the served users. Numerical results show that the multi-user multiple input, single output (MU-MISO) system split between a GBS and an AR with optimized transmission parameters without knowledge of the eavesdropping channels achieves high secrecy capacities that scale well with increasing the number of users.
Toward Fair Facial Expression Recognition with Improved Distribution Alignment
Venue: arXiv cs.CV 2023
Year: 2023

We present a novel approach to mitigate bias in facial expression recognition (FER) models. Our method aims to reduce sensitive attribute information such as gender, age, or race, in the embeddings produced by FER models. We employ a kernel mean shrinkage estimator to estimate the kernel mean of the distributions of the embeddings associated with different sensitive attribute groups, such as young and old, in the Hilbert space. Using this estimation, we calculate the maximum mean discrepancy (MMD) distance between the distributions and incorporate it in the classifier loss along with an adversarial loss, which is then minimized through the learning process to improve the distribution alignment. Our method makes sensitive attributes less recognizable for the model, which in turn promotes fairness. Additionally, for the first time, we analyze the notion of attractiveness as an important sensitive attribute in FER models and demonstrate that FER models can indeed exhibit biases towards more attractive faces. To prove the efficacy of our model in reducing bias regarding different sensitive attributes (including the newly proposed attractiveness attribute), we perform several experiments on two widely used datasets, CelebA and RAF-DB. The results in terms of both accuracy and fairness measures outperform the state-of-the-art in most cases, demonstrating the effectiveness of the proposed method.
PWR-Align: Leveraging Part-Whole Relationships for Part-wise Rigid Point Cloud Registration in Mixed Reality Applications
Venue: arXiv cs.CV 2023
Year: 2023

We present an efficient and robust point cloud registration (PCR) workflow for part-wise rigid point cloud alignment using the Microsoft HoloLens 2. Point Cloud Registration (PCR) is an important problem in Augmented and Mixed Reality use cases, and we present a study for a special class of non-rigid transformations. Many commonly encountered objects are composed of rigid parts that move relative to one another about joints resulting in non-rigid deformation of the whole object such as robots with manipulators, and machines with hinges. The workflow presented allows us to register the point cloud with various configurations of the point cloud.
$E(2)$-Equivariant Vision Transformer
Venue: arXiv cs.CV 2023
Year: 2023

Vision Transformer (ViT) has achieved remarkable performance in computer vision. However, positional encoding in ViT makes it substantially difficult to learn the intrinsic equivariance in data. Initial attempts have been made on designing equivariant ViT but are proved defective in some cases in this paper. To address this issue, we design a Group Equivariant Vision Transformer (GE-ViT) via a novel, effective positional encoding operator. We prove that GE-ViT meets all the theoretical requirements of an equivariant neural network. Comprehensive experiments are conducted on standard benchmark datasets, demonstrating that GE-ViT significantly outperforms non-equivariant self-attention networks. The code is available at https://github.com/ZJUCDSYangKaifan/GEVit.
Generating One-Hot Maps under Encryption
Venue: arXiv cs.CR 2023
Year: 2023

One-hot maps are commonly used in the AI domain. Unsurprisingly, they can also bring great benefits to ML-based algorithms such as decision trees that run under Homomorphic Encryption (HE), specifically CKKS. Prior studies in this domain used these maps but assumed that the client encrypts them. Here, we consider different tradeoffs that may affect the client's decision on how to pack and store these maps. We suggest several conversion algorithms when working with encrypted data and report their costs. Our goal is to equip the ML over HE designer with the data it needs for implementing encrypted one-hot maps.
Adversarial Reconnaissance Mitigation and Modeling
Venue: arXiv cs.CR 2023
Year: 2023

Adversarial reconnaissance is a crucial step in sophisticated cyber-attacks as it enables threat actors to find the weakest points of otherwise well-defended systems. To thwart reconnaissance, defenders can employ cyber deception techniques, such as deploying honeypots. In recent years, researchers have made great strides in developing game-theoretic models to find optimal deception strategies. However, most of these game-theoretic models build on relatively simple models of adversarial reconnaissance -- even though reconnaissance should be a focus point as the very purpose of deception is to thwart reconnaissance. In this paper, we first discuss effective cyber reconnaissance mitigation techniques including deception strategies and beyond. Then we provide a review of the literature on deception games from the perspective of modeling adversarial reconnaissance, highlighting key aspects of reconnaissance that have not been adequately captured in prior work. We then describe a probability-theory based model of the adversaries' belief formation and illustrate using numerical examples that this model can capture key aspects of adversarial reconnaissance. We believe that our review and belief model can serve as a stepping stone for developing more realistic and practical deception games.
To Save Mobile Crowdsourcing from Cheap-talk: A Game Theoretic Learning Approach
Venue: arXiv cs.GT 2023
Year: 2023

Today mobile crowdsourcing platforms invite users to provide anonymous reviews about service experiences, yet many reviews are found biased to be extremely positive or negative. The existing methods find it difficult to learn from biased reviews to infer the actual service state, as the state can also be extreme and the platform cannot verify the truthfulness of reviews immediately. Further, reviewers can hide their (positive or negative) bias types and proactively adjust their anonymous reviews against the platform's inference. To our best knowledge, we are the first to study how to save mobile crowdsourcing from cheap-talk and strategically learn from biased users' reviews. We formulate the problem as a dynamic Bayesian game, including users' service-type messaging and the platform's follow-up rating/inference. Our closed-form PBE shows that an extremely-biased user may still honestly message to convince the platform of listening to his review. Such Bayesian game-theoretic learning obviously outperforms the latest common schemes especially when there are multiple diversely-biased users to compete. For the challenging single-user case, we further propose a time-evolving mechanism with the platform's commitment inferences to ensure the biased user's truthful messaging all the time, whose performance improves with more time periods to learn from more historical data.
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing
Venue: arXiv cs.CL 2023
Year: 2023

Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP). In this work, we present AraMUS, the largest Arabic PLM with 11B parameters trained on 529GB of high-quality Arabic textual data. AraMUS achieves state-of-the-art performances on a diverse set of Arabic classification and generative tasks. Moreover, AraMUS shows impressive few-shot learning abilities compared with the best existing Arabic PLMs.
Stable Remaster: Bridging the Gap Between Old Content and New Displays
Venue: arXiv cs.CV 2023
Year: 2023

The invention of modern displays has enhanced the viewer experience for any kind of content: ranging from sports to movies in 8K high-definition resolution. However, older content developed for CRT or early Plasma screen TVs has become outdated quickly and no longer meets current aspect ratio and resolution standards. In this paper, we explore whether we can solve this problem with the use of diffusion models to adapt old content to meet contemporary expectations. We explore the ability to combine multiple independent computer vision tasks to attempt to solve the problem of expanding aspect ratios of old animated content such that the new content would be indistinguishable from the source material to a brand-new viewer. These existing capabilities include Stable Diffusion, Content-Aware Scene Detection, Object Detection, and Key Point Matching. We were able to successfully chain these tasks together in a way that generated reasonable outputs, however, future work needs to be done to improve and expand the application to non-animated content as well.